A review

Funding Information: PhD grant PD/BDE/150627/2020 was financed by Fundação para a Ciência e Tecnologia (FCT - Portugal) and Volkswagen Autoeuropa. Funding Information: The authors would like to thank Fundação para a Ciência e Tecnologia (FCT - Portugal), and Volkswagen Autoeuropa for co-financing the doctoral grant PD/BDE/150627/2020. Publisher Copyright: © 2023 The AuthorsIon Mobility Spectrometry (IMS) has gained relevance in the field of analytical techniques over the past decades. If compared with well-established techniques like mass spectrometry or infrared spectroscopy, IMS is considerably less developed or employed in specific fields but presents promising results and a substantial margin for improvements. Its outstanding sensitivity and selectivity, analytical flexibility, instrumental versatility and almost real-time results capacity have contributed to elevate IMS among the main analytical techniques for the detection of volatile organic compounds. Due to its growth potential, it is mandatory to assess in which scientific fields IMS has played a relevant role in the past years of academic research and understand in which areas it can become equally important in the near future. For this purpose, hundreds of scientific works from the past ten years were addressed and the most relevant were reviewed in this work. Three main categories of IMS applications were defined to group the reviewed articles: Environmental and Safety Research, Health Research and Food Research. In addition, some original studies were specifically developed for this review paper, to act as elucidative examples. The working principle of the IMS is included for clarification purposes. A glossary of all the mentioned compounds was also included. Throughout the text, it is clear how relevant IMS has become and how diverse its applicability can be, ranging from simpler topics like fraud detection to more complex ones like pathologies diagnosis. It is safe to say that IMS has been, step by step, gaining relevance as an analytical technique and its potential for supporting many diverse scientific fields is evident.publishersversionpublishe

A university campus case study

Funding Information: The authors would like to thank Fundação para a Ciência e Tecnologia (FCT - Portugal), Volkswagen Autoeuropa and NMT, S. A. for co-financing of PhD grant PD/BDE/150627/2020, from Doctoral NOVA I4H Program. Funding Information: The author(s) disclosed receipt of the following financial support for the research, authorship, and/or publication of this article: This work was supported by Fundação para a Ciência e Tecnologia FCT – Portugal and Volkswagen Autoeuropa - Portugal, which co-financed the PhD grant PD/BDE/150627/2020 from Doctoral NOVA I4H Program; and NMT, S. A. – Portugal for the supply of the GC-IMS device and additional resources. Publisher Copyright: © The Author(s) 2022.Society’s concerns about the citizens’ exposure to possibly dangerous environments have recently risen; nevertheless, the assessment of indoor air quality still represents a major contemporary challenge. The volatile organic compounds (VOCs) are among the main factors responsible for deteriorating air quality conditions. These analytes are very common in daily-use environments and they can be extremely hazardous to human health, even at trace concentrations levels. For these reasons, their quick detection, identification, and quantification are crucial tasks, especially for indoor and heavily-populated scenarios, where the exposure time is usually quite long. In this work, a Gas Chromatography – Ion Mobility Spectrometry (GC-IMS) device was used for continuous monitoring indoor and ambient air environments at a large-scale, due to its outstanding levels of sensibility, selectivity, analytical flexibility, and almost real-time monitoring capability. A total of 496 spectra were collected from 15 locations of a university campus and posteriorly analysed. Overall, 23 compounds were identified among the 31 detected. Some of them, like Ethanol and 2-Propanol, were reported as being very hazardous to the human organism, especially in indoor environments. The achieved results confirmed the suitability of GC-IMS technology for air quality assessment and monitoring of VOCs and, more importantly, proved how dangerous indoor environments can be in scenarios of continuous exposure.publishersversionpublishe

A gas chromatography–ion mobility spectrometry applicability study

The authors acknowledge NMT. S. A. for supplying the GC-IMS device and additional resources. Publisher Copyright: © The Author(s) 2023.Contemporary life is mostly spent in indoor spaces like private houses, workplaces, vehicles and public facilities. Nonetheless, the air quality in these closed environments is often poor which leads to people being exposed to a vast range of toxic and hazardous compounds. Volatile organic compounds (VOCs) are among the main factors responsible for the lack of air quality in closed spaces and, in addition, some of them are particularly hazardous to the human organism. Considering this fact, we conducted daily in situ air analyses over 1 year using a gas chromatography–ion mobility spectrometry (GC-IMS) device in an indoor location. The obtained results show that 10 VOCs were consistently present in the indoor air throughout the entire year, making them particularly important for controlling air quality. All of these compounds were successfully identified, namely acetic acid, acetone, benzene, butanol, ethanol, isobutanol, propanoic acid, propanol, 2-propanol and tert-butyl methyl ether. The behaviour of the total VOCs (tVOCs) intensity during the period of analysis and the relative variation between consecutive months were studied. It was observed that the overall trend of tVOCs closely mirrored the variation of air temperature throughout the year suggesting their strong correlation. The results obtained from this study demonstrate the high quality and relevance of the data, highlighting the suitability of GC-IMS for in situ long-term air quality assessment in indoor environments and, consequently, for identifying potential health risks for the human organism in both short-term and long-term exposure scenarios.publishersversionpublishe

On the Feasibility of Real-Time HRV Estimation Using Overly Noisy PPG Signals

Funding Information: This work was funded by the Fundação para a Ciência e Tecnologia (FCT, Portugal) and NMT, S.A in the scope of the PhD grant PD/BDE/150312/2019 and by FCT within the scope of the CTS Research Unit—Center of Technology and Systems—UNINOVA, under the project UIDB/00066/2020 (FCT). Publisher Copyright: © 2022 by the authors.Heart Rate Variability (HRV) is a biomarker that can be obtained non-invasively from the electrocardiogram (ECG) or the photoplethysmogram (PPG) fiducial points. However, the accuracy of HRV can be compromised by the presence of artifacts. In the herein presented work, a Simulink® model with a deep learning component was studied for overly noisy PPG signals. A subset with these noisy signals was selected for this study, with the purpose of testing a real-time machine learning based HRV estimation system in substandard artifact-ridden signals. Home-based and wearable HRV systems are prone to dealing with higher contaminated signals, given the less controlled environment where the acquisitions take place, namely daily activity movements. This was the motivation behind this work. The results for overly noisy signals show that the real-time PPG-based HRV estimation system produced RMSE and Pearson correlation coefficient mean and standard deviation of 0.178 ± 0.138 s and 0.401 ± 0.255, respectively. This RMSE value is roughly one order of magnitude above the closest comparative results for which the real-time system was also used.publishersversionpublishe

The application of deep learning algorithms for PPG signal processing and classification

Funding Information: This research was funded by the Funda??o para a Ci?ncia e Tecnologia (FCT, Lisbon, Portugal) and NMT, S.A in the scope of the PhD grant PD/BDE/150312/2019 and by FCT within the scope of the CTS Research Unit?Center of Technology and Systems?UNINOVA, under the project UIDB/00066/2020 (FCT). Funding Information: Funding: This research was funded by the Fundação para a Ciência e Tecnologia (FCT, Lisbon, Portugal) and NMT, S.A in the scope of the PhD grant PD/BDE/150312/2019 and by FCT within the scope of the CTS Research Unit—Center of Technology and Systems—UNINOVA, under the project UIDB/00066/2020 (FCT). Publisher Copyright: © 2021 by the authorsLicensee MDPI, Basel, Switzerland.Photoplethysmography (PPG) is widely used in wearable devices due to its conveniency and cost-effective nature. From this signal, several biomarkers can be collected, such as heart and respiration rate. For the usual acquisition scenarios, PPG is an artefact-ridden signal, which mandates the need for the designated classification algorithms to be able to reduce the noise component effect on the classification. Within the selected classification algorithm, the hyperparameters’ adjustment is of utmost importance. This study aimed to develop a deep learning model for robust PPG wave detection, which includes finding each beat’s temporal limits, from which the peak can be determined. A study database consisting of 1100 records was created from experimental PPG measurements performed in 47 participants. Different deep learning models were implemented to classify the PPG: Long Short-Term Memory (LSTM), Bidirectional LSTM, and Convolutional Neural Network (CNN). The Bidirectional LSTM and the CNN-LSTM were investigated, using the PPG Synchrosqueezed Fourier Transform (SSFT) as the models’ input. Accuracy, precision, recall, and F1-score were evaluated for all models. The CNN-LSTM algorithm, with an SSFT input, was the best performing model with accuracy, precision, and recall of 0.894, 0.923, and 0.914, respectively. This model has shown to be competent in PPG detection and delineation tasks, under noise-corrupted signals, which justifies the use of this innovative approach.publishersversionpublishe

Continuous measurements of central blood pressure during mental stress monitoring

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Peak Detection and HRV Feature Evaluation on ECG and PPG Signals

Publisher Copyright: © 2022 by the authors. Licensee MDPI, Basel, Switzerland.Heart Rate Variability (HRV) evaluates the autonomic nervous system regulation and can be used as a monitoring tool in conditions such as cardiovascular diseases, neuropathies and sleep staging. It can be extracted from the electrocardiogram (ECG) and the photoplethysmogram (PPG) signals. Typically, the HRV is obtained from the ECG processing. Being the PPG sensor widely used in clinical setups for physiological parameters monitoring such as blood oxygenation and ventilatory rate, the question arises regarding the PPG adequacy for HRV extraction. There is not a consensus regarding the PPG being able to replace the ECG in the HRV estimation. This work aims to be a contribution to this research area by comparing the HRV estimation obtained from simultaneously acquired ECG and PPG signals from forty subjects. A peak detection method is herein introduced based on the Hilbert transform: Hilbert Double Envelope Method (HDEM). Two other peak detector methods were also evaluated: Pan-Tompkins and Wavelet-based. HRV parameters for time, frequency and the non-linear domain were calculated for each algorithm and the Pearson correlation, T-test and RMSE were evaluated. The HDEM algorithm showed the best overall results with a sensitivity of 99.07% and 99.45% for the ECG and the PPG signals, respectively. For this algorithm, a high correlation and no significant differences were found between HRV features and the gold standard, for the ECG and PPG signals. The results show that the PPG is a suitable alternative to the ECG for HRV feature extraction.publishersversionpublishe

P6 CFD Modelling of Arterialized Venous Flap

The knowledge about the required quantity of blood to irrigate an angiosome is important on ischemia (disruption on blood perfusion) prediction, diagnosis and treatment. An angiosome (or flap) is an anatomic unity (or flap) of tissue, it is constituted by skin, subcutaneous tissue and muscle, it is irrigated by an artery and drain by specific veins [1]. Since 70’s, flaps have been used on clinical practice for reconstruction of complex anatomical structures. Different model configurations have been created, to find a flap’s model that allows a better flap perfusion. In previous work [2] the four models with an average flap survival area of 76.86% ± 13.67% were tested in 53 male rats: I - conventional model of flap’s blood supply formed by femoral and epigastric arteries; II – Arterialized Venous Flap (AVF) produced by femoral side-to-side anastomosis; III - AVF produced by femoral side-to-side anastomosis and proximal ligation of the femoral vein; IV - AVF produced by terminal lateral anastomosis of the epigastric vein to the femoral artery). The experimental results have shown that the AVFs in group IV represent an optimized model of unconventional perfusion flap. In the present work the Computational Fluid Dynamics (CFD) methods, an ANSYS®-Fluent code, were used for simulating a blood flow and flap perfusion in AVFs of group IV in order to find an optimum geometry for lateral anastomosis of the epigastric vein to the femoral artery with an angle variation from 90,0º to 45,0º. We find that the optimum angle is 86,5º. Three other models, conventional and unconventional, was also tested by CFD, finding that unconventional AVF of group III provides a greater blood flow through the epigastric vein, allowing a better perfusion of the flap.publishersversionpublishe

Differentiation of the Organoleptic Volatile Organic Compound Profile of Three Edible Seaweeds

Funding Information: This research was funded by FUNDAÇÃO PARA A CIÊNCIA E TECNOLOGIA (FCT—PORTUGAL), grant number PD/BDE/150627/2020. This research was funded by MAR2020 – PORTUGUESE GOVERNMENT, project number MAR-01.03.0-FEAMP-0016. Funding Information: P.C.M. acknowledges Fundação para a Ciência e Tecnologia (FCT—Portugal) for his doctoral grant (PD/BDE/150627/2020). Publisher Copyright: © 2023 by the authors.The inclusion of seaweeds in daily-consumption food is a worthy-of-attention challenge due to their high nutritional value and potential health benefits. In this way, their composition, organoleptic profile, and toxicity must be assessed. This work focuses on studying the volatile organic compounds (VOCs) emitted by three edible seaweeds, Grateloupia turuturu, Codium tomentosum, and Bifurcaria bifurcata, with the aim of deepening the knowledge regarding their organoleptic profiles. Nine samples of each seaweed were prepared in glass vials, and the emitted headspace was analyzed, for the first time, with a gas chromatography—ion mobility spectrometry device, a highly sensitive technology. By statistically processing the collected data through PCA, it was possible to accurately differentiate the characteristic patterns of the three seaweeds with a total explained variance of 98%. If the data were pre-processed through PLS Regression, the total explained variance increased to 99.36%. The identification of 13 VOCs was accomplished through a developed database of compounds. These outstanding values in addition to the identification of the main emissions of VOCs and the utilization of a never-before-used technology prove the capacity of GC-IMS to differentiate edible seaweeds based solely on their volatile emissions, increase the knowledge regarding their organoleptic profiles, and provide an important step forward in the inclusion of these highly nutritional ingredients in the human diet.publishersversionpublishe